#include <algorithm>
#include <cfloat>
#include <vector>

#include "caffe/layers/temp_softmax_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>
void TempSoftmaxWithLossLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::LayerSetUp(bottom, top);

  temperature = this->layer_param_.tempsoftmax_param().temperature();
  CHECK_GT(temperature, 1)
      << "Gradient assumes a (high) softmax temperature of greater than 1.";

  LayerParameter softmax_param(this->layer_param_);
  softmax_param.set_type("Softmax");


  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
  softmax_bottom_vec_.clear();
  softmax_bottom_vec_.push_back(bottom[0]);
  softmax_top_vec_.clear();
  softmax_top_vec_.push_back(&prob_);
  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);

  softmax_target_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
  softmax_target_bottom_vec_.clear();
  softmax_target_bottom_vec_.push_back(bottom[1]);
  softmax_target_top_vec_.clear();
  softmax_target_top_vec_.push_back(&target_prob_);
  softmax_target_layer_->SetUp(softmax_target_bottom_vec_,
          softmax_target_top_vec_);
/*
  has_ignore_label_ =
    this->layer_param_.loss_param().has_ignore_label();
  if (has_ignore_label_) {
    ignore_label_ = this->layer_param_.loss_param().ignore_label();
  }
*/
  if (!this->layer_param_.loss_param().has_normalization() &&
      this->layer_param_.loss_param().has_normalize()) {
    normalization_ = this->layer_param_.loss_param().normalize() ?
                     LossParameter_NormalizationMode_VALID :
                     LossParameter_NormalizationMode_BATCH_SIZE;
  } else {
    normalization_ = this->layer_param_.loss_param().normalization();
  }
}

template <typename Dtype>
void TempSoftmaxWithLossLayer<Dtype>::Reshape(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
  softmax_target_layer_->Reshape(softmax_target_bottom_vec_,
      softmax_target_top_vec_);

/*
  softmax_axis_ =
      bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
  outer_num_ = bottom[0]->count(0, softmax_axis_);
  inner_num_ = bottom[0]->count(softmax_axis_ + 1);
*/

  CHECK_EQ(bottom[0]->count(), bottom[1]->count())
    <<"TEMP_SOFTMAX_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
  /*CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
      << "Number of labels must match number of predictions; "
      << "e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), "
      << "label count (number of labels) must be N*H*W, "
      << "with integer values in {0, 1, ..., C-1}.";*/
  if (top.size() >= 2) {
    // softmax output
    top[1]->ReshapeLike(*bottom[0]);
    if(top.size() >=3){
        top[2]->ReshapeLike(*bottom[1]);
    }
  }
}

/*
template <typename Dtype>
Dtype TempTempSoftmaxWithLossLayer<Dtype>::get_normalizer(
    LossParameter_NormalizationMode normalization_mode, int valid_count) {
  Dtype normalizer;
  switch (normalization_mode) {
    case LossParameter_NormalizationMode_FULL:
      normalizer = Dtype(outer_num_ * inner_num_);
      break;
    case LossParameter_NormalizationMode_VALID:
      if (valid_count == -1) {
        normalizer = Dtype(outer_num_ * inner_num_);
      } else {
        normalizer = Dtype(valid_count);
      }
      break;
    case LossParameter_NormalizationMode_BATCH_SIZE:
      normalizer = Dtype(outer_num_);
      break;
    case LossParameter_NormalizationMode_NONE:
      normalizer = Dtype(1);
      break;
    default:
      LOG(FATAL) << "Unknown normalization mode: "
          << LossParameter_NormalizationMode_Name(normalization_mode);
  }
  // Some users will have no labels for some examples in order to 'turn off' a
  // particular loss in a multi-task setup. The max prevents NaNs in that case.
  return std::max(Dtype(1.0), normalizer);
}
*/

template <typename Dtype>
void TempSoftmaxWithLossLayer<Dtype>::Forward_cpu(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  
  // Logits divide by temperature.
  const int count = bottom[0]->count();

  caffe_scal(count, Dtype(1) / temperature,
                    softmax_bottom_vec_[0]->mutable_cpu_data());

  caffe_scal(count, Dtype(1) / temperature,
                    softmax_target_bottom_vec_[0]->mutable_cpu_data());

  // The forward pass computes the softmax prob values.
  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
  softmax_target_layer_->Forward(softmax_target_bottom_vec_,
                    softmax_target_top_vec_);

  const Dtype* prob_data = prob_.cpu_data();
  const Dtype* target_prob = target_prob_.cpu_data();

  //const Dtype* label = bottom[1]->cpu_data();

  //int dim = prob_.count() / outer_num_;
  //int count = 0;
  const int num = bottom[0]->num();
  Dtype loss = 0;
  for(int i=0; i < count; i++){
    loss -= target_prob[i] * log(std::max(prob_data[i] ,Dtype(FLT_MIN)));
  }
  /*for (int i = 0; i < outer_num_; ++i) {
    for (int j = 0; j < inner_num_; j++) {
      const int label_value = static_cast<int>(label[i * inner_num_ + j]);
      if (has_ignore_label_ && label_value == ignore_label_) {
        continue;
      }
      DCHECK_GE(label_value, 0);
      DCHECK_LT(label_value, prob_.shape(softmax_axis_));
      loss -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],
                           Dtype(FLT_MIN)));
      ++count;
    }
  }*/
  top[0]->mutable_cpu_data()[0] = loss / num; //get_normalizer(normalization_, count);

  if (top.size() >= 2) {
    top[1]->ShareData(prob_);
      if (top.size() >= 3) {
        top[2]->ShareData(target_prob_);
      }
  }

}

template <typename Dtype>
void TempSoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
  if (propagate_down[1]) {
    LOG(FATAL) << this->type()
               << " Layer cannot backpropagate to target inputs.";
  }
  if (propagate_down[0]) {
    // First, compute the diff 
    const int count = bottom[0]->count();
    const int num = bottom[0]->num();

    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    const Dtype* prob_data = prob_.cpu_data();
    const Dtype* target_prob = target_prob_.cpu_data();

    // bottom_diff = prob_data - target_prob; qi - pi
    caffe_sub(count, prob_data, target_prob, bottom_diff);
    
    // bottom_diff = bottom_diff / temperature
    caffe_scal(count, Dtype(1)/temperature, bottom_diff);
/*
    const Dtype* label = bottom[1]->cpu_data();
    int dim = prob_.count() / outer_num_;
    int count = 0;
    for (int i = 0; i < outer_num_; ++i) {
      for (int j = 0; j < inner_num_; ++j) {
        const int label_value = static_cast<int>(label[i * inner_num_ + j]);
        if (has_ignore_label_ && label_value == ignore_label_) {
          for (int c = 0; c < bottom[0]->shape(softmax_axis_); ++c) {
            bottom_diff[i * dim + c * inner_num_ + j] = 0;
          }
        } else {
          bottom_diff[i * dim + label_value * inner_num_ + j] -= 1;
          ++count;
        }
      }
    }
*/
    // Scale gradient
    Dtype loss_weight = top[0]->cpu_diff()[0] / num;
                        //get_normalizer(normalization_, count);
    caffe_scal(prob_.count(), loss_weight, bottom_diff);
  }
}

#ifdef CPU_ONLY
STUB_GPU(TempSoftmaxWithLossLayer);
#endif

INSTANTIATE_CLASS(TempSoftmaxWithLossLayer);
REGISTER_LAYER_CLASS(TempSoftmaxWithLoss);

}  // namespace caffe
